code: def simulate_multi_motif_embedding(
      motif_names,
      seq_length,
      min_num_motifs,
      max_num_motifs,
      num_seqs,
      GC_fraction,
  ):
      """Generates data for multi motif recognition task.
  
      Parameters
      ----------
      motif_names : list
          List of strings.
      seq_length : int
      min_num_motifs : int
      max_num_motifs : int
      num_seqs : int
      GC_fraction : float
  
      Returns
      -------
      sequence_arr : 1darray
          Contains sequence strings.
      y : ndarray
          Contains labels for each motif.
      embedding_arr: 1darray
          Array of embedding objects.
      """
  
      import simdna
      from simdna import synthetic
  
      loaded_motifs = synthetic.LoadedEncodeMotifs(
          simdna.ENCODE_MOTIFS_PATH, pseudocountProb=0.001
      )
  
      def get_embedder(motif_name):
          substring_generator = synthetic.PwmSamplerFromLoadedMotifs(
              loaded_motifs, motif_name
          )
          return synthetic.SubstringEmbedder(
              synthetic.ReverseComplementWrapper(substring_generator),
              name=motif_name,
          )
  
      embedders = [
          get_embedder(motif_name) for motif_name in motif_names
      ]
      quantity_generator = synthetic.UniformIntegerGenerator(
          min_num_motifs, max_num_motifs
      )
      combined_embedder = [
          synthetic.RandomSubsetOfEmbedders(
              quantity_generator, embedders
          )
      ]
      embed_in_background = synthetic.EmbedInABackground(
          synthetic.ZeroOrderBackgroundGenerator(
              seq_length,
              discreteDistribution=get_distribution(GC_fraction),
          ),
          combined_embedder,
      )
      generated_sequences = tuple(
          synthetic.GenerateSequenceNTimes(
              embed_in_background, num_seqs
          ).generateSequences()
      )
      sequence_arr = np.array(
          [generated_seq.seq for generated_seq in generated_sequences]
      )
      label_generator = synthetic.IsInTraceLabelGenerator(
          np.asarray(motif_names)
      )
      y = np.array(
          [
              label_generator.generateLabels(generated_seq)
              for generated_seq in generated_sequences
          ],
          dtype=bool,
      )
      embedding_arr = [
          generated_seq.embeddings
          for generated_seq in generated_sequences
      ]
      return sequence_arr, y, embedding_arr
  
file_path: deepchem/deepchem/molnet/dnasim.py
imports: import numpy
label_desc: Use image node_id 7 for calling a global function with example usage: simulate_multi_motif_embedding(motif_names, seq_length, min_num_motifs, max_num_motifs, num_seqs, GC_fraction) and returns: sequence_arr,  y,  embedding_arr, synthetic